Evidence map›Paper›PMID 42787370›Full record

ArticleAlzheimer's & dementia (Amsterdam, Netherlands)

Comprehensive evaluation of AT(N) imaging biomarkers for predicting cognitive decline.

Tom Earnest, Braden Yang, Deydeep Kothapalli, Alzheimer's Disease Neuroimaging Initiative, Aristeidis Sotiras

Abstract read
In one paragraph

Article in Alzheimer's & dementia (Amsterdam, Netherlands). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Tom EarnestMallinckrodt Institute of Radiology Washington University School of Medicine in St Louis Saint Louis Missouri USA.ORCID https://orcid.org/0000-0001-8671-8424
Braden YangMallinckrodt Institute of Radiology Washington University School of Medicine in St Louis Saint Louis Missouri USA.ORCID https://orcid.org/0000-0002-2558-4132
Deydeep KothapalliMallinckrodt Institute of Radiology Washington University School of Medicine in St Louis Saint Louis Missouri USA.
Alzheimer's Disease Neuroimaging Initiative
Aristeidis SotirasMallinckrodt Institute of Radiology Washington University School of Medicine in St Louis Saint Louis Missouri USA.ORCID https://orcid.org/0000-0003-0795-8820

Funding

Project 1U19AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE/RES/EDU · PI RICHARD Justin PERRIN · 2016 to 2026
$226.7M
Advanced machine learning algorithms that integrate multi-modal neuroimaging to quantify the heterogeneity in Alzheimer's DiseaseR01AG067103 · NIA · WASHINGTON UNIVERSITY · PI SOTIRAS, ARISTEIDIS · 2021 to 2025
$2.8M
HIGH PERFORMANCE BIOMEDICAL IMAGING COMPUTER RESOURCESS10RR022984 · NCRR · WASHINGTON UNIVERSITY · PI PRIOR, FRED WILLIAM · 2009 to 2009
$1.9M
A High Performance Research Image Repository (RIR) for the Washington University Center of High Performance Computing (CHPC)S10OD030477 · OD · WASHINGTON UNIVERSITY · PI MARCUS, DANIEL SCOTT · 2021 to 2021
$1.9M
GPU COMPUTING RESOURCE TO ENABLE INNOVATION IN IMAGING AND NETWORK BIOLOGYS10OD018091 · OD · WASHINGTON UNIVERSITY · PI PAPPU, ROHIT V, PRIOR, FRED WILLIAM · 2014 to 2014
$598k
Acquisition of a next-generation computing clusterS10OD025200 · OD · WASHINGTON UNIVERSITY · PI TOBIAS, MALCOLM · 2019 to 2019
$597k
NCRR NIH HHS S10 RR022984NIA NIH HHS R01 AG067103NIA NIH HHS U19 AG024904NIH HHS S10 OD018091NIH HHS S10 OD025200NIH HHS S10 OD030477
6 · The paper itself

Abstract

introductionFew studies have investigated how different Alzheimer's disease biomarker definitions affect predictions of cognitive decline.

methodsWe predicted cognitive decline using 42 biomarker definitions spanning amyloid beta, tau, and neurodegeneration in 383 ADNI participants with baseline neuroimaging and longitudinal cognitive assessments. We identified optimal predictors for each pathology and variable type (continuous, binary, non-binary categorical) using nested cross-validation. Models were compared against support vector machines (SVMs) incorporating brain-wide pathology. In a subsample, we compared imaging-based biomarkers with biofluids (cerebrospinal fluid and plasma).

resultsWe observed substantial variability in accuracy, even for measures of the same pathology. Tau biomarkers were most accurate, performing comparably to models incorporating all three pathologies. Binarization of biomarkers mostly preserved accuracy, while SVMs trained on brain-wide pathology data generally did not improve performance. Imaging-based biomarkers consistently outperformed biofluid ones. DISCUSSION: Imaging-based biomarker definitions are not interchangeable and can influence the assessment of cognitive decline.

Indexed as

Alzheimer's diseaseamyloidbinarizationcognitive declinemachine learningmagnetic resonance imagingMRIneurodegenerationPETpositron emission tomographytau

Identifiers

PMID42787370
PMCPMC13601734

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.